The Center for Nanobiology and Predictive Toxicology has assembled a multidisciplinary team with expertise in nanomaterial science, toxicology, cell biology, high throughput screening, biostatistics, mathematics and computer science with the overall goal of gaining fundamental understanding of how the physical and Chemical properties of carefully selected ENM libraries relate to interactions with cells and cellular structures, including how these bio-physicochemical interactions at the nano-bio interface may lead to pulmonary toxicity. This goal will be executed through the acquisition, synthesis and characterization of compositional and combinatorial ENM libraries that focus on the major physicochemical properties of nominated metal, metal oxide and silica nanoparticles {Scientific Core), hypothesized to play a role in pulmonary toxicity through the generation of oxidative stress, inflammation, signal pathway activation and membrane lysis. These efforts will be assisted by in silico modeling that use heatmaps, mathematical models and machine learning to perform hazard ranking and risk prediction. The major objectives of the Center are: (i) To establish an overarching predictive toxicological paradigm, which is defined as the assessment of in vivo toxic potential of ENM based on in vitro and in silico methods (integrated center effort);(ii) To establish rapid throughput cellular screening and conduct imaging to identify compositional and combinatorial ENM properties that lead to bioavailability and engagement of the injury pathways discussed above (Project 1);(iii) To establish through the performance of instillation and inhalation exposures in the rodent lung how the structure-property relationships linking ENM to in vitro injury mechanisms may be predictive of pulmonary inflammation, fibrosis and cytotoxicity in a dose-dependent fashion (Project 2);(iv) To develop in silico toxicity models that utilize multivariate analysis of the rapid throughput screening and cellular imaging data to show the relationships that can be used to develop "nano-QSARs" for probabilistic risk ranking (Project 3).